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                        <h1 class="single-title flipInX">TensorFlow2.1入门学习笔记(13)——卷积神经网络LeNet, AlexNet, VGGNet示例</h1><div class="post-meta summary-post-meta"><span class="post-category meta-item">
                                <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
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                                <span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-06-15">2020-06-15</time>
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                                <span class="svg-icon icon-pencil"></span>约 2564 字
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                                <span class="svg-icon icon-stopwatch"></span>预计阅读 6 分钟
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                            <span>目录</span>
                        </div>
                        <div class="details-content toc-content" id="toc-content-static"><nav id="TableOfContents">
  <ul>
    <li><a href="#cifar10数据集">Cifar10数据集</a></li>
    <li><a href="#搭建网络">搭建网络</a></li>
    <li><a href="#lenet">LeNet</a></li>
    <li><a href="#alexnet">AlexNet</a></li>
    <li><a href="#vggnet">VGGNet</a></li>
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                    </div><h2 id="cifar10数据集" class="headerLink"><a href="#cifar10%e6%95%b0%e6%8d%ae%e9%9b%86" class="header-mark"></a>Cifar10数据集</h2><ul>
<li>提供 5万张 32*32 像素点的十分类彩色图片和标签，用于训练。</li>
<li>提供 1万张 32*32 像素点的十分类彩色图片和标签，用于测试。</li>
</ul>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614104318623.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614104318623.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>导入cifar10数据集：</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span><span class="lnt">2
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
</code></pre></td></tr></table>
</div>
</div><ul>
<li>可视化训练集输入特征的第一个元素</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span><span class="lnt">2
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="c1">#绘制图片</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614115315796.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614115315796.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>打印出训练集输入特征的第一个元素</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">print</span><span class="p">(</span><span class="s2">&#34;x_train[0]:</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/2020061411570171.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/2020061411570171.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>打印出训练集标签的第一个元素</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">print</span><span class="p">(</span><span class="s2">&#34;y_train[0]:</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614120554157.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614120554157.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>打印出整个训练集输入特征形状</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">print</span><span class="p">(</span><span class="s2">&#34;x_train.shape:</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614120443118.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614120443118.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>打印出整个训练集标签的形状</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">print</span><span class="p">(</span><span class="s2">&#34;y_train.shape:</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614120702274.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614120702274.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>打印出整个测试集输入特征的形状</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">print</span><span class="p">(</span><span class="s2">&#34;x_test.shape:</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614120811931.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614120811931.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>打印出整个测试集标签的形状</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt">1
</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">print</span><span class="p">(</span><span class="s2">&#34;y_test.shape:</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</code></pre></td></tr></table>
</div>
</div><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614120857899.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614120857899.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<h2 id="搭建网络" class="headerLink"><a href="#%e6%90%ad%e5%bb%ba%e7%bd%91%e7%bb%9c" class="header-mark"></a>搭建网络</h2><p>利用cifar10数据集搭建一个网络，训练模型</p>
<ul>
<li>网络设计：
卷积层：6个5x5，步长为1，使用全零填充的卷积核；2个2x2，步长为2，使用全零填充的最大值池化核；20%的神经元休眠（暂时舍弃）。
全连接层：先将卷积训练的数据拉直；送入128个神经元，激活函数为“relu”，20%休眠的全连接；再将数据送入10个神经元，符合概率分布的全连接。</li>
</ul>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614121632633.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614121632633.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<ul>
<li>源码</li>
</ul>
<div class="highlight"><div class="chroma">
<table class="lntable"><tr><td class="lntd">
<pre class="chroma"><code><span class="lnt"> 1
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<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">BatchNormalization</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">MaxPool2D</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Model</span>

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>

<span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>


<span class="k">class</span> <span class="nc">Baseline</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Baseline</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>  <span class="c1"># 卷积层</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>  <span class="c1"># BN层</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">a1</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>  <span class="c1"># 激活层</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p1</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>  <span class="c1"># 池化层</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">d1</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>  <span class="c1"># dropout层</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">f1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">d2</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">f2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">y</span>


<span class="n">model</span> <span class="o">=</span> <span class="n">Baseline</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
              <span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">])</span>

<span class="n">checkpoint_save_path</span> <span class="o">=</span> <span class="s2">&#34;./checkpoint/Baseline.ckpt&#34;</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">checkpoint_save_path</span> <span class="o">+</span> <span class="s1">&#39;.index&#39;</span><span class="p">):</span>
    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;-------------load the model-----------------&#39;</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">checkpoint_save_path</span><span class="p">)</span>

<span class="n">cp_callback</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="n">checkpoint_save_path</span><span class="p">,</span>
                                                 <span class="n">save_weights_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
                                                 <span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cp_callback</span><span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="c1"># print(model.trainable_variables)</span>
<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./weights.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">:</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="c1">###############################################    show   ###############################################</span>

<span class="c1"># 显示训练集和验证集的acc和loss曲线</span>
<span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

</code></pre></td></tr></table>
</div>
</div><ul>
<li>运行结果</li>
</ul>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614161153169.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614161153169.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614161257461.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614161257461.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614161743929.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614161743929.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<h2 id="lenet" class="headerLink"><a href="#lenet" class="header-mark"></a>LeNet</h2><p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614174456990.gif" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614174456990.gif"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>是由Yann LeCun于1998年提出，是卷积网络的开篇之作</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614174636853.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614174636853.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络结构</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614174900641.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614174900641.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络搭建</strong></p>
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</span></code></pre></td>
<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">class</span> <span class="nc">LeNet5</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
	<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
		<span class="nb">super</span><span class="p">(</span><span class="n">LeNet5</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="o">=</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">fliters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="o">=</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c2</span><span class="o">=</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="o">=</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="o">=</span><span class="n">Flatten</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d1</span><span class="o">=</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d2</span><span class="o">=</span><span class="n">Dense</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d3</span><span class="o">=</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>
	<span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">c2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">y</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="k">return</span> <span class="n">y</span>
</code></pre></td></tr></table>
</div>
</div><p><strong>示例：</strong></p>
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<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPool2D</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Model</span>

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>

<span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">/</span><span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span><span class="o">/</span><span class="mf">255.0</span>


<span class="k">class</span> <span class="nc">LeNet5</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
	<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
		<span class="nb">super</span><span class="p">(</span><span class="n">LeNet5</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		
		<span class="bp">self</span><span class="o">.</span><span class="n">p1</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c2</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		
		<span class="bp">self</span><span class="o">.</span><span class="n">p2</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d3</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>

	<span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="k">return</span> <span class="n">y</span>	


<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet5</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
			  <span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">])</span>

<span class="n">checkpoint_save_path</span> <span class="o">=</span> <span class="s2">&#34;./checkpoint/LeNet5.ckpt&#34;</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">checkpoint_save_path</span> <span class="o">+</span> <span class="s1">&#39;.index&#39;</span><span class="p">):</span>
	<span class="k">print</span><span class="p">(</span><span class="s1">&#39;-------------load the model-----------------&#39;</span><span class="p">)</span>
	<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">checkpoint_save_path</span><span class="p">)</span>

<span class="n">cp_callback</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span> <span class="o">=</span> <span class="n">checkpoint_save_path</span><span class="p">,</span>
												 <span class="n">save_weights_only</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
												 <span class="n">save_best_only</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
					<span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cp_callback</span><span class="p">])</span>

<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="c1"># print(model.trainable_variables)</span>
<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./weights.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">:</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="c1">###############################################    show   ###############################################</span>

<span class="c1"># 显示训练集和测试集的acc和loss曲线</span>
<span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valiation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Train and Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></td></tr></table>
</div>
</div><p><strong>运行结果</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614193821748.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614193821748.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614193558383.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614193558383.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<h2 id="alexnet" class="headerLink"><a href="#alexnet" class="header-mark"></a>AlexNet</h2><p><a href="http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf" target="_blank" rel="noopener noreffer">AlexNet</a>网络诞生于2012年，当年ImageNet竞赛的冠军，Top5错误率为16.4%
使用“relu”激活函数，提升了训练速度，使用Dropout缓解过拟合</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614194424177.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614194424177.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络结构</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614195430131.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614195430131.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络搭建示例</strong></p>
<div class="highlight"><div class="chroma">
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<pre class="chroma"><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">BatchNormalization</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">MaxPool2D</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Model</span>

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>

<span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>


<span class="k">class</span> <span class="nc">AlexNet8</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">AlexNet8</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">96</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">a1</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p1</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">c2</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">b2</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">a2</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p2</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">c3</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">384</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span>
                         <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
                         
        <span class="bp">self</span><span class="o">.</span><span class="n">c4</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">384</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span>
                         <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
                         
        <span class="bp">self</span><span class="o">.</span><span class="n">c5</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span>
                         <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p3</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">f1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">2048</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">d1</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">f2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">2048</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">d2</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">f3</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c5</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">y</span>


<span class="n">model</span> <span class="o">=</span> <span class="n">AlexNet8</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
              <span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">])</span>

<span class="n">checkpoint_save_path</span> <span class="o">=</span> <span class="s2">&#34;./checkpoint/AlexNet8.ckpt&#34;</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">checkpoint_save_path</span> <span class="o">+</span> <span class="s1">&#39;.index&#39;</span><span class="p">):</span>
    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;-------------load the model-----------------&#39;</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">checkpoint_save_path</span><span class="p">)</span>

<span class="n">cp_callback</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="n">checkpoint_save_path</span><span class="p">,</span>
                                                 <span class="n">save_weights_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
                                                 <span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cp_callback</span><span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="c1"># print(model.trainable_variables)</span>
<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./weights.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">:</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="c1">###############################################    show   ###############################################</span>

<span class="c1"># 显示训练集和验证集的acc和loss曲线</span>
<span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

</code></pre></td></tr></table>
</div>
</div><p><strong>运行结果</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614233214119.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614233214119.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200614233440782.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200614233440782.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615000653501.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615000653501.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<h2 id="vggnet" class="headerLink"><a href="#vggnet" class="header-mark"></a>VGGNet</h2><p>VGGNet诞生于2014年，当年ImageNet竞赛的亚军，Top5错误率减小到7.3%
使用小尺寸卷积核，在减少参数的同时提高了识别的准确率，网络规整适合硬件加速</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615104857151.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615104857151.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络结构</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615105332275.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615105332275.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络搭建示例</strong></p>
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<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span> 
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">MaxPool2D</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">BatchNormalization</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span> 

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>

<span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>


<span class="k">class</span> <span class="nc">VGGNet4</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
	<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
		<span class="nb">super</span><span class="p">(</span><span class="n">VGGNet4</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a1</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c2</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b2</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a2</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p1</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d1</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c3</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b3</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a3</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c4</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b4</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a4</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p2</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d2</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c5</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b5</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a5</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c6</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b6</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a6</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c7</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b7</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a7</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p3</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d3</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c8</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b8</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a8</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c9</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b9</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a9</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c10</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b10</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a10</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p4</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d4</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c11</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b11</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a11</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c12</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b12</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a12</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c13</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b13</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a13</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p5</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d5</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">f1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d6</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">f2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">d7</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">f3</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>

	<span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c5</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b5</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a5</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c6</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b6</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a6</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>


		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c7</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b7</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a7</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c8</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b8</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a8</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c9</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b9</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a9</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c10</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b10</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a10</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c11</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b11</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a11</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c12</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b12</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a12</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c13</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b13</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a13</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p5</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d5</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d6</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d7</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="k">return</span> <span class="n">y</span> 

<span class="n">model</span> <span class="o">=</span> <span class="n">VGGNet4</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
				<span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
				<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">])</span>

<span class="n">checkpoint_save_path</span> <span class="o">=</span> <span class="s1">&#39;./checkpoint/AGGNet4.ckpt&#39;</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">checkpoint_save_path</span> <span class="o">+</span> <span class="s1">&#39;.index&#39;</span><span class="p">):</span>
	<span class="k">print</span><span class="p">(</span><span class="s1">&#39;--------------- load the model -----------------&#39;</span><span class="p">)</span>
	<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">checkpoint_save_path</span><span class="p">)</span>

<span class="n">cp_callabck</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="n">checkpoint_save_path</span><span class="p">,</span>
												<span class="n">save_weights_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
												<span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> 
					<span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cp_callabck</span><span class="p">])</span>

<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./weights.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">:</span>
	<span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
	<span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
	<span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

</code></pre></td></tr></table>
</div>
</div><ul>
<li>运行结果</li>
</ul>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615122443271.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615122443271.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615132027172.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615132027172.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615122343826.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615122343826.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
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